Kim Soo Min, Naqvi Syed Dildar Haider, Kang Min Gu, Song Hee-Eun, Ahn SeJin
Nano Electronic Materials & Components Research Center, IT Materials & Components Research Division, Gumi Electronics & Information Technology Research Institute (GERI), Gumi 39171, Korea.
Photovoltaics Research Department, Korea Institute of Energy Research (KIER), Daejeon 34129, Korea.
Nanomaterials (Basel). 2022 Mar 11;12(6):932. doi: 10.3390/nano12060932.
Quaternary perovskite solar cells are being extensively studied, with the goal of increasing solar cell efficiency and securing stability by changing the ratios of methylammonium, formamidinium, I, and Br. However, when the stoichiometric ratio is changed, the photoelectric properties reflect those of different materials, making it difficult to study the physical properties of the quaternary perovskite. In this study, the optical properties of perovskite materials with various stoichiometric ratios were measured using ellipsometry, and the results were analyzed using an optical simulation model. Because it is difficult to analyze the spectral pattern according to composition using the existing method of statistical regression analysis, an artificial neural network (ANN) structure was constructed to enable the hyperregression analysis of n-dimensional variables. Finally, by inputting the stoichiometric ratios used in the fabrication and the wavelength range to the trained artificial intelligence model, it was confirmed that the optical properties were similar to those measured with an ellipsometer. The refractive index and extinction coefficient extracted through the ellipsometry analysis show a tendency consistent with the color change of the specimen, and have a similar shape to that reported in the literature. When the optical properties of the unmodified perovskite are predicted using the verified artificial intelligence model, a very complex change in pattern is observed, which is impossible to analyze with a general regression method. It can be seen that this change in optical properties is well maintained, even during rapid variations in the pattern according to the change in composition. In conclusion, hyperregression analysis with n-dimensional variables can be performed for the spectral patterns of thin-film materials using a simple big data construction method.
四元钙钛矿太阳能电池正在被广泛研究，其目标是通过改变甲铵、甲脒、碘和溴的比例来提高太阳能电池效率并确保稳定性。然而，当化学计量比改变时，光电特性反映出不同材料的特性，这使得研究四元钙钛矿的物理性质变得困难。在本研究中，使用椭偏仪测量了具有各种化学计量比的钙钛矿材料的光学性质，并使用光学模拟模型对结果进行了分析。由于使用现有的统计回归分析方法难以根据成分分析光谱模式，因此构建了人工神经网络（ANN）结构以实现n维变量的超回归分析。最后，将制造中使用的化学计量比和波长范围输入到经过训练的人工智能模型中，证实其光学性质与用椭偏仪测量的性质相似。通过椭偏仪分析提取的折射率和消光系数显示出与样品颜色变化一致的趋势，并且形状与文献报道的相似。当使用经过验证的人工智能模型预测未改性钙钛矿的光学性质时，观察到非常复杂的模式变化，这是用一般回归方法无法分析的。可以看出，即使在根据成分变化而快速变化的模式期间，这种光学性质的变化也能很好地保持。总之，使用简单的大数据构建方法可以对薄膜材料的光谱模式进行n维变量的超回归分析。
